Java Code Examples for org.apache.commons.math3.exception.util.LocalizedFormats#NO_REGRESSORS

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Example 1
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)
throws ModelSpecificationException {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 2
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 * @throws ModelSpecificationException if {@code nreq} is less than 1
 * or greater than the number of independent variables
 */
private double[] regcf(int nreq) throws ModelSpecificationException {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    final double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (FastMath.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 3
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance) {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 4
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 */
private double[] regcf(int nreq) {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 5
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance) {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 6
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 */
private double[] regcf(int nreq) {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 7
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)
throws ModelSpecificationException {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 8
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 * @throws ModelSpecificationException if {@code nreq} is less than 1
 * or greater than the number of independent variables
 */
private double[] regcf(int nreq) throws ModelSpecificationException {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    final double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 9
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance) {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
    return;
}
Example 10
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 */
private double[] regcf(int nreq) {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 11
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)
throws ModelSpecificationException {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 12
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 * @throws ModelSpecificationException if {@code nreq} is less than 1
 * or greater than the number of independent variables
 */
private double[] regcf(int nreq) throws ModelSpecificationException {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    final double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 13
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)
throws ModelSpecificationException {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 14
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 * @throws ModelSpecificationException if {@code nreq} is less than 1
 * or greater than the number of independent variables
 */
private double[] regcf(int nreq) throws ModelSpecificationException {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    final double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (Math.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}
Example 15
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * This is the augmented constructor for the MillerUpdatingRegression class.
 *
 * @param numberOfVariables number of regressors to expect, not including constant
 * @param includeConstant include a constant automatically
 * @param errorTolerance  zero tolerance, how machine zero is determined
 * @throws ModelSpecificationException if {@code numberOfVariables is less than 1}
 */
public MillerUpdatingRegression(int numberOfVariables, boolean includeConstant, double errorTolerance)
throws ModelSpecificationException {
    if (numberOfVariables < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (includeConstant) {
        this.nvars = numberOfVariables + 1;
    } else {
        this.nvars = numberOfVariables;
    }
    this.hasIntercept = includeConstant;
    this.nobs = 0;
    this.d = new double[this.nvars];
    this.rhs = new double[this.nvars];
    this.r = new double[this.nvars * (this.nvars - 1) / 2];
    this.tol = new double[this.nvars];
    this.rss = new double[this.nvars];
    this.vorder = new int[this.nvars];
    this.x_sing = new double[this.nvars];
    this.work_sing = new double[this.nvars];
    this.work_tolset = new double[this.nvars];
    this.lindep = new boolean[this.nvars];
    for (int i = 0; i < this.nvars; i++) {
        vorder[i] = i;
    }
    if (errorTolerance > 0) {
        this.epsilon = errorTolerance;
    } else {
        this.epsilon = -errorTolerance;
    }
}
Example 16
Source File: MillerUpdatingRegression.java    From astor with GNU General Public License v2.0 5 votes vote down vote up 
/**
 * The regcf method conducts the linear regression and extracts the
 * parameter vector. Notice that the algorithm can do subset regression
 * with no alteration.
 *
 * @param nreq how many of the regressors to include (either in canonical
 * order, or in the current reordered state)
 * @return an array with the estimated slope coefficients
 * @throws ModelSpecificationException if {@code nreq} is less than 1
 * or greater than the number of independent variables
 */
private double[] regcf(int nreq) throws ModelSpecificationException {
    int nextr;
    if (nreq < 1) {
        throw new ModelSpecificationException(LocalizedFormats.NO_REGRESSORS);
    }
    if (nreq > this.nvars) {
        throw new ModelSpecificationException(
                LocalizedFormats.TOO_MANY_REGRESSORS, nreq, this.nvars);
    }
    if (!this.tol_set) {
        tolset();
    }
    final double[] ret = new double[nreq];
    boolean rankProblem = false;
    for (int i = nreq - 1; i > -1; i--) {
        if (FastMath.sqrt(d[i]) < tol[i]) {
            ret[i] = 0.0;
            d[i] = 0.0;
            rankProblem = true;
        } else {
            ret[i] = rhs[i];
            nextr = i * (nvars + nvars - i - 1) / 2;
            for (int j = i + 1; j < nreq; j++) {
                ret[i] = smartAdd(ret[i], -r[nextr] * ret[j]);
                ++nextr;
            }
        }
    }
    if (rankProblem) {
        for (int i = 0; i < nreq; i++) {
            if (this.lindep[i]) {
                ret[i] = Double.NaN;
            }
        }
    }
    return ret;
}